Traditional entity linkers mainly depend on manually designed features to evaluate the local context compatibility and document-level global coherence of referent entities [1]}, [2]}.
The design of such features requires entity-specific domain knowledge. These features can not fully capture relevant statistical dependencies and interactions.
One recent notable work [3]} instead pioneers to rely on pre-trained entity embeddings, learnable context representation and differentiable joint inference stage to learn basic features and their combinations from scratch. Such model design allows to learn useful regularities in an end-to-end fashion and eliminates the need for extensive feature engineering.
It also substantially outperforms the traditional methods on standard benchmark (e.g., AIDA-CoNLL).
A line of follow-up work [4]}, [5]}, [6]} investigate potential improvement solution or other task settings based on that.
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